If only we had ten more listicles, they thought… then we could have done a listicle of 16 listicles! <sigh> Here are our top slide decks/videos (this was the year we introduced the “mini-MOOC”), a few bonus corresponding podcasts to help us round things out, and finally, all our “listicles” from this past year — beginning with a few that are as true for 2017 as they were in 2016…
“If we could climb into a time machine, journey 30 years into the future, and from that vantage look back to today, we’d realize that most of the greatest products running the lives of citizens in 2050 were not invented until after 2016… Right now, today, in 2016 is the best time to start up. There has never been a better day in the whole history of the world to invent something.” (Kevin Kelly)
The most important companies — including the next generation of big franchises — are likely going to end up being public. And being IPO-ready can actually give founders more options and more control over their destiny. But when should companies go public? The answer is simple: when they’re ready. In fact, to-IPO-or-not-to-IPO is not the right question; the question is how to be ready and ensure that a business is “working”. So think of this as public investors’ wishlist for companies seeking to IPO; regardless of desired outcome, it’s a checklist for building a robust and enduring business…
The basic mechanics of how the VC industry works remain opaque even to the entrepreneurs who interact with the industry on a daily basis. This post tries to clear up some of that opacity by defining 16 basic terms, beginning with a bit of history — from Queen Isabella of Spain (arguably the first true VC) backing Christopher Columbus, to the early days of the U.S. whaling industry.
There’s a new era of social self-expression and mobile entertainment happening, enabled by always-on cameras, real-time computer vision, social media, and other tech. Several of these phenomena — from emoji to stickers to filters to cultural memes — emerged in Asia or were popularized in messaging apps there before they made their way into global products. So what’s next? Well, livestreaming is currently exploding in China. And while it’s been around in the U.S. for years in various forms, it hasn’t really taken off at mainstream scale… yet. So what can we learn as livestreaming evolves in China (and perhaps here too)?
Why are these forms of social communication so popular? Because sometimes you just want to say “I feel totally Nicki Minaj side-eye dot-GIF about this” … and no one can give a side-eye as good as Nicki Minaj can. But it’s not just about isolated expressions, celebrity stickers like Kimoji, or personalized bitmoji; stickers are shaping and codifying the way people talk to each other online in new and multi-layered ways. It’s even connected to mobile livestreaming, a phenomenon that’s taking off in China right now, in the most mundane (food eating streams) to subversive (seductive banana eating streams) ways…
Whatever your views about 2016, there’s no denying it was a momentous year on the policy front. Seemingly each passing day brought noteworthy developments in the relationship between the tech industry and lawmakers at the federal, state, local, and international levels. Here’s the a16z Policy and Regulatory Affairs team’s take on the biggest technology policy developments of the past year, from the elections and Brexit to the fight over backdoors and the fight over APIs and more.
videos and slide decks (+bonus podcasts)
Things are progressing more rapidly than ever on the distributed systems and machine intelligence front. But how did we get here, after not one but multiple “A.I. winters”? What’s the “why now” breakthrough; why is Silicon Valley buzzing about artificial intelligence again? From types of machine intelligence to a tour of algorithms, Frank Chen ( head of a16z’s deal, research, and investing team) walks us through the basics (and beyond) of AI and deep learning in this enormously popular slide presentation/ video.
Whether it’s checkers, chess, Jeopardy, or (most recently) the ancient Chinese game of Go, we get excited about the potential of A.I. when we see computers beat humans. But then nothing “big” — in terms of generalized artificial intelligence — seems to happen after that initial burst of excitement and enthusiasm. Frank Chen and board partner Steven Sinofsky — who both suffered through the last AI winter — share how everything old is new again; the triumph of data over algorithms; and the evergreen battle between purist vs. “practical” approaches when it comes to implementing new tech at production scale.
Whoa… wait a minute: How can we say cloud computing is coming to an “end” when it hasn’t even really started yet?? Because the edge — where self-driving cars and drones are really data centers with wheels and wings — is where it’s at. Where does machine learning in the enterprise come in? How does this change IT? As software programs the world, these are some of the shifts to look at…
We already know mobile is eating the world as we pass 2.5 billion smartphones on earth and head towards 5 billion. And not only is the s-curve of innovation for mobile passing PCs, but it’s moving into the deployment phase. Which means the questions have shifted from “Will this work?” and “Who will win?” … to “What can we build with this?” So what happens when this new kind of scale for technology — and new kinds of computers (with cameras and sensors in everything) — changes other industries, like cars and commerce for example? What happens as companies move from “mobile-first” to “AI-first” with machine learning and more?
“Infrastructure is dead”, some say, thanks to cloud computing and a couple large incumbents sucking out all the profits in this space. Er… not really. As three key trends come together — hardware to software, software as services, and rise of the developer — we’re actually entering a renaissance of sorts, a “golden era of infrastructure” … and it’s one that is biased towards startups. One of the fathers of SDN or software-defined networking, a16z general partner Martin Casado shares what happens when you put a “software-defined” in front of everything: No silo is safe. And it’s not just storage; it’s computing, networking, security, management, databases, analytics, development, and so on.
Infrastructure. It powers everything from cities to computing, yet is sometimes considered “boring” because it is so invisible to so many of us. But as software continues to eat the world, infrastructure has come to the forefront. And some of the most exciting technology innovations are now happening at the infrastructure level: It’s changing everything, from how new tech is created to how new tech is sold.
One of the most important concepts for business in general and especially for tech businesses, network effects are the key dynamic behind many successful software-based companies. Understanding them not only helps build better products, but helps build moats and protect software companies against competitors’ eating away at their margins. Yet what IS a network effect? And how do we know a company has network effects?
One of the biggest misconceptions around network effects is confusing growth with engagement. So how does one tell the difference between viral growth and network effects? How does one create network effects in different businesses? (Hint: it’s not by accident!) How do you know when to hang in there because you see signs of network effects … or just drop it and move on to something else? And what are some company/product examples of teasing all of this apart?
The bio industry is evolving and tech will play a large part in its next chapter. In fact, biology is looking more and more like programming lately, from “digital therapeutics” to “computational biomedicine” and “cloud bio”. But what’s the difference between computer science x bio (as opposed to the ‘biotech’ of yore)? And how does this affect investing in bio startups? (Hint: software lets you de-risk at every stage.)
Nature can handle all sorts of complex calculations that computers can’t (or that would take a prohibitively long time and resources). But quantum computing isn’t just about being able to do more with computers in a faster way: It’s about solving problems that we couldn’t solve with traditional computers; it’s about a difference of kind, not just of degree. So what is a quantum computer and “qubits” — especially as compared to a traditional computer and bits? And what are some of the potential applications of quantum computing?